Pop nat analysis
Laure Olazcuaga, Nicolas Rode
20-June-2022
1 DATA
#############################
#PERFORMANCE
#############################
data_PERF <- import_data(dataset = "DATACOMPLET_PERF.csv",
trait = "performance",
remove_testenvt = c("Grape","GF"),
remove_pop = c("WT3"),
remove_rate = NA)## [1] "Data (541 and 602 tubes for the first and third generation respectively) where i) the number of eggs was NA (5 and 0 tubes for the first and third generation respectively); or ii) the number of adults was NA (0 and 0 tubes for the first and third generation respectively); or iii) the number of eggs was zero -Emergence rate = NaN- (99 and 0 tubes for the first and third generation respectively); or iv) the number of adults was higher than the initial number of eggs (50 and 1 tubes for the first and third generation respectively) were not removed."head(data_PERF)## Generation Experiment Original_environment Population Date_P Date_C_O
## 8 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 13 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 14 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 16 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 20 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 24 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## Date_C_A Row Column Rack Test_environment Nb_eggs Obs_O Nb_adults Obs_A
## 8 19/10/2018 L1 C3 1 Cherry 3 LO 2 LO
## 13 19/10/2018 L1 C8 1 Blackberry 1 LO 2 LO
## 14 19/10/2018 L1 C9 1 Cherry 2 LO 1 LO
## 16 19/10/2018 L2 C1 1 Blackberry 1 LO 0 LO
## 20 19/10/2018 L2 C5 1 Strawberry 2 LO 2 LO
## 24 19/10/2018 L2 C9 1 Cherry 1 LO 1 LO
## EggScore EggScoreFive EggScoreSmall SA IndicG0 IndicG2 SAIndicG0
## 8 1 1 1 0 1 0 0
## 13 1 1 1 1 1 0 1
## 14 1 1 1 0 1 0 0
## 16 1 1 1 1 1 0 1
## 20 1 1 1 0 1 0 0
## 24 1 1 1 0 1 0 0
## fruit_hab fruit_hab_ng fruit_gen hab_gen
## 8 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 13 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0 Blackberry_G0
## 14 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 16 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0 Blackberry_G0
## 20 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0 Strawberry_G0
## 24 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## pop_gen Rate
## 8 Blackberry45_G0 0.6666667
## 13 Blackberry45_G0 2.0000000
## 14 Blackberry45_G0 0.5000000
## 16 Blackberry45_G0 0.0000000
## 20 Blackberry45_G0 1.0000000
## 24 Blackberry45_G0 1.0000000tapply(data_PERF$Nb_adults,list(data_PERF$Original_environment,data_PERF$Generation),length)## G0 G2
## Blackberry 169 320
## Cherry 233 143
## Strawberry 35 139#Problem eggs
data_issue_eggs <- data_PERF[data_PERF$Nb_adults>data_PERF$Nb_eggs,]
#Ovservations with more eggs than edults
dim(data_issue_eggs)[1]## [1] 51#Ovservations with a difference equal or less than two eggs
sum(ifelse((data_issue_eggs$Nb_adults-data_issue_eggs$Nb_eggs)<=2,1,0))## [1] 44tapply(data_issue_eggs$Nb_eggs,data_issue_eggs$Test_environment,length)## Blackberry Cherry Strawberry
## 18 17 16tapply(data_issue_eggs$Nb_eggs,data_issue_eggs$Original_environment,length)## Blackberry Cherry Strawberry
## 41 9 1tapply(data_PERF$Nb_eggs,data_PERF$Original_environment,length)## Blackberry Cherry Strawberry
## 489 376 174mean(data_issue_eggs$Rate)## [1] 2.44571data_issue_eggs$Nb_eggs## [1] 1 1 2 1 1 1 1 1 1 3 3 1 2 1 5 1 1 3 2 1 2 3 2 1 2
## [26] 1 3 1 1 1 1 2 2 2 1 2 3 1 1 2 1 1 9 4 2 18 1 1 1 3
## [51] 78data_issue_eggs$Nb_adults## [1] 2 3 4 3 3 2 2 2 2 5 5 2 4 12 7 2 13 5 3 2 3 5 6 2 3
## [26] 2 4 3 3 2 3 5 4 3 4 4 5 3 2 3 2 2 14 5 3 30 2 2 2 5
## [51] 80data_issue_eggs$Rate## [1] 2.000000 3.000000 2.000000 3.000000 3.000000 2.000000 2.000000
## [8] 2.000000 2.000000 1.666667 1.666667 2.000000 2.000000 12.000000
## [15] 1.400000 2.000000 13.000000 1.666667 1.500000 2.000000 1.500000
## [22] 1.666667 3.000000 2.000000 1.500000 2.000000 1.333333 3.000000
## [29] 3.000000 2.000000 3.000000 2.500000 2.000000 1.500000 4.000000
## [36] 2.000000 1.666667 3.000000 2.000000 1.500000 2.000000 2.000000
## [43] 1.555556 1.250000 1.500000 1.666667 2.000000 2.000000 2.000000
## [50] 1.666667 1.025641#############################
#EMERGENCE RATE
#############################
#When Eggs>Adults: remove data
data_PERF_Rate_removed <- import_data(dataset = "DATACOMPLET_PERF.csv",
trait = "performance",
remove_testenvt = c("Grape","GF"),
remove_pop = c("WT3"),
remove_rate = TRUE)## [1] "Data (541 and 602 tubes for the first and third generation respectively) where i) the number of eggs was NA (5 and 0 tubes for the first and third generation respectively); or ii) the number of adults was NA (0 and 0 tubes for the first and third generation respectively); or iii) the number of eggs was zero -Emergence rate = NaN- (99 and 0 tubes for the first and third generation respectively); or iv) the number of adults was higher than the number of eggs (50 and 1 tubes for the first and third generation respectively) were removed."head(data_PERF_Rate_removed)## Generation Experiment Original_environment Population Date_P Date_C_O
## 8 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 14 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 16 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 20 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 24 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 33 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## Date_C_A Row Column Rack Test_environment Nb_eggs Obs_O Nb_adults Obs_A
## 8 19/10/2018 L1 C3 1 Cherry 3 LO 2 LO
## 14 19/10/2018 L1 C9 1 Cherry 2 LO 1 LO
## 16 19/10/2018 L2 C1 1 Blackberry 1 LO 0 LO
## 20 19/10/2018 L2 C5 1 Strawberry 2 LO 2 LO
## 24 19/10/2018 L2 C9 1 Cherry 1 LO 1 LO
## 33 19/10/2018 L3 C8 1 Blackberry 2 LO 2 LO
## EggScore EggScoreFive EggScoreSmall SA IndicG0 IndicG2 SAIndicG0
## 8 1 1 1 0 1 0 0
## 14 1 1 1 0 1 0 0
## 16 1 1 1 1 1 0 1
## 20 1 1 1 0 1 0 0
## 24 1 1 1 0 1 0 0
## 33 1 1 1 1 1 0 1
## fruit_hab fruit_hab_ng fruit_gen hab_gen
## 8 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 14 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 16 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0 Blackberry_G0
## 20 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0 Strawberry_G0
## 24 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 33 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0 Blackberry_G0
## pop_gen Rate
## 8 Blackberry45_G0 0.6666667
## 14 Blackberry45_G0 0.5000000
## 16 Blackberry45_G0 0.0000000
## 20 Blackberry45_G0 1.0000000
## 24 Blackberry45_G0 1.0000000
## 33 Blackberry45_G0 1.0000000tapply(data_PERF_Rate_removed$Nb_adults,list(data_PERF_Rate_removed$Original_environment,
data_PERF_Rate_removed$Generation),length)## G0 G2
## Blackberry 129 319
## Cherry 224 143
## Strawberry 34 139tapply(data_PERF_Rate_removed$Rate,data_PERF_Rate_removed$Generation,mean)## G0 G2
## 0.3593365 0.1857181#When Eggs>Adults: Rate=1
data_PERF_Rate <- import_data(dataset = "DATACOMPLET_PERF.csv",
trait = "performance",
remove_testenvt = c("Grape","GF"),
remove_pop = c("WT3"),
remove_rate = "Replace")## [1] "Data (541 and 602 tubes for the first and third generation respectively) where i) the number of eggs was NA (5 and 0 tubes for the first and third generation respectively); or ii) the number of adults was NA (0 and 0 tubes for the first and third generation respectively); or iii) the number of eggs was zero -Emergence rate = NaN- (99 and 0 tubes for the first and third generation respectively); or iv) the number of adults was higher than the initial number of eggs (50 and 1 tubes for the first and third generation respectively) were not removed but the emergence rate was REPLACED by 1."tapply(data_PERF_Rate$Nb_adults,list(data_PERF_Rate$Original_environment,
data_PERF_Rate$Generation),length)## G0 G2
## Blackberry 169 320
## Cherry 233 143
## Strawberry 35 139tapply(data_PERF_Rate$Rate,data_PERF_Rate$Generation,mean)## G0 G2
## 0.4326390 0.1870708###########################
#PREFERENCE
###########################
data_PREF <- import_data(dataset = "DATACOMPLET_PREF.csv",
trait = "preference",
remove_testenvt = NA,
remove_pop = c("WT3"),
remove_rate = NA)
head(data_PREF)## Generation Experiment BoxID Date_P Original_environment Population Line
## 1 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 2 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 3 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 4 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 5 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 2
## 6 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 2
## Column Test_environment Nb_eggs Date_C_O Obs_O SA IndicG0 IndicG2 SAIndicG0
## 1 1 Cranberry 0 13/12/2018 CD 0 1 0 0
## 2 2 Fig 0 13/12/2018 CD 0 1 0 0
## 3 3 Raspberry 0 13/12/2018 CD 0 1 0 0
## 4 4 Rosehips 0 13/12/2018 CD 0 1 0 0
## 5 1 Kiwi 0 13/12/2018 CD 0 1 0 0
## 6 2 Strawberry 1 13/12/2018 CD 0 1 0 0
## fruit_hab fruit_hab_ng fruit_gen hab_gen
## 1 Blackberry_Cranberry Blackberry_Cranberry_G0 Blackberry_G0 Cranberry_G0
## 2 Blackberry_Fig Blackberry_Fig_G0 Blackberry_G0 Fig_G0
## 3 Blackberry_Raspberry Blackberry_Raspberry_G0 Blackberry_G0 Raspberry_G0
## 4 Blackberry_Rosehips Blackberry_Rosehips_G0 Blackberry_G0 Rosehips_G0
## 5 Blackberry_Kiwi Blackberry_Kiwi_G0 Blackberry_G0 Kiwi_G0
## 6 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0 Strawberry_G0
## pop_gen
## 1 Blackberry33_G0
## 2 Blackberry33_G0
## 3 Blackberry33_G0
## 4 Blackberry33_G0
## 5 Blackberry33_G0
## 6 Blackberry33_G0tapply(data_PREF$Nb_eggs,list(data_PREF$Original_environment,data_PREF$Generation),length)## G0 G2
## Blackberry 696 1176
## Cherry 1200 624
## Strawberry 252 480###########################
#PREFERENCE 3 fruits
###########################
levels_test<-levels(data_PREF$Test_environment)
levels_original<-levels(data_PREF$Original_environment)
data_PREF_three <- import_data(dataset = "DATACOMPLET_PREF.csv",
trait = "preference",
remove_testenvt = usefun::outersect(levels_test,
levels_original),
remove_pop = c("WT3"),
remove_rate = NA)
head(data_PREF_three)## Generation Experiment BoxID Date_P Original_environment Population
## 6 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33
## 9 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33
## 11 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33
## 15 G0 Plasticity 884 19/09/2018 Blackberry Blackberry33
## 16 G0 Plasticity 884 19/09/2018 Blackberry Blackberry33
## 20 G0 Plasticity 884 19/09/2018 Blackberry Blackberry33
## Line Column Test_environment Nb_eggs Date_C_O Obs_O SA IndicG0 IndicG2
## 6 2 2 Strawberry 1 13/12/2018 CD 0 1 0
## 9 3 1 Blackberry 0 13/12/2018 CD 1 1 0
## 11 3 3 Cherry 0 13/12/2018 CD 0 1 0
## 15 1 3 Cherry 0 13/12/2018 CD 0 1 0
## 16 1 4 Strawberry 0 13/12/2018 CD 0 1 0
## 20 2 4 Blackberry 0 13/12/2018 CD 1 1 0
## SAIndicG0 fruit_hab fruit_hab_ng fruit_gen
## 6 0 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0
## 9 1 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0
## 11 0 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0
## 15 0 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0
## 16 0 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0
## 20 1 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0
## hab_gen pop_gen
## 6 Strawberry_G0 Blackberry33_G0
## 9 Blackberry_G0 Blackberry33_G0
## 11 Cherry_G0 Blackberry33_G0
## 15 Cherry_G0 Blackberry33_G0
## 16 Strawberry_G0 Blackberry33_G0
## 20 Blackberry_G0 Blackberry33_G0tapply(data_PREF_three$Nb_eggs,list(data_PREF_three$Original_environment,
data_PREF_three$Generation),length)## G0 G2
## Blackberry 174 294
## Cherry 300 156
## Strawberry 63 120resume_design<-tapply(as.factor(data_PREF_three$BoxID),list(data_PREF_three$Population,
data_PREF_three$Generation),length)
mean(resume_design[,1], na.rm = TRUE)/3## [1] 7.782609mean(resume_design[,2], na.rm = TRUE)/3## [1] 7.916667resume_design<-tapply(data_PERF$Nb_eggs,list(data_PERF$Population,
data_PERF$Generation),length)
mean(resume_design[,1], na.rm = TRUE)/3## [1] 6.069444mean(resume_design[,2], na.rm = TRUE)/3## [1] 8.026667dim(data_PREF_three)## [1] 1107 21dim(data_PREF)## [1] 4428 21head(data_PREF)## Generation Experiment BoxID Date_P Original_environment Population Line
## 1 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 2 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 3 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 4 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 5 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 2
## 6 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 2
## Column Test_environment Nb_eggs Date_C_O Obs_O SA IndicG0 IndicG2 SAIndicG0
## 1 1 Cranberry 0 13/12/2018 CD 0 1 0 0
## 2 2 Fig 0 13/12/2018 CD 0 1 0 0
## 3 3 Raspberry 0 13/12/2018 CD 0 1 0 0
## 4 4 Rosehips 0 13/12/2018 CD 0 1 0 0
## 5 1 Kiwi 0 13/12/2018 CD 0 1 0 0
## 6 2 Strawberry 1 13/12/2018 CD 0 1 0 0
## fruit_hab fruit_hab_ng fruit_gen hab_gen
## 1 Blackberry_Cranberry Blackberry_Cranberry_G0 Blackberry_G0 Cranberry_G0
## 2 Blackberry_Fig Blackberry_Fig_G0 Blackberry_G0 Fig_G0
## 3 Blackberry_Raspberry Blackberry_Raspberry_G0 Blackberry_G0 Raspberry_G0
## 4 Blackberry_Rosehips Blackberry_Rosehips_G0 Blackberry_G0 Rosehips_G0
## 5 Blackberry_Kiwi Blackberry_Kiwi_G0 Blackberry_G0 Kiwi_G0
## 6 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0 Strawberry_G0
## pop_gen
## 1 Blackberry33_G0
## 2 Blackberry33_G0
## 3 Blackberry33_G0
## 4 Blackberry33_G0
## 5 Blackberry33_G0
## 6 Blackberry33_G0levels(data_PREF$Population)## [1] "Blackberry31" "Blackberry32" "Blackberry33" "Blackberry34" "Blackberry35"
## [6] "Blackberry36" "Blackberry37" "Blackberry38" "Blackberry39" "Blackberry40"
## [11] "Blackberry43" "Blackberry44" "Blackberry45" "Cherry103" "Cherry104"
## [16] "Cherry3" "Cherry47" "Cherry50" "Cherry51" "Cherry52"
## [21] "Cherry6" "Cherry7" "Strawberry42" "Strawberry44" "Strawberry53"2 FECUNDITY
2.1 Exploration data
glmm::binomial.glmm()$family.glmm## [1] "binomial.glmm"tapply(data_PERF$Nb_eggs, list(data_PERF$Original_environment, data_PERF$Test_environment, data_PERF$Generation), mean, na.rm = TRUE)## , , G0
##
## Blackberry Cherry Strawberry
## Blackberry 6.52459 10.22034 7.306122
## Cherry 23.35443 19.77500 12.283784
## Strawberry 21.30769 33.60000 16.833333
##
## , , G2
##
## Blackberry Cherry Strawberry
## Blackberry 103.9720 121.8491 98.71963
## Cherry 131.7174 133.8400 103.51064
## Strawberry 119.1739 119.4894 111.56522ggplot2::ggplot(data = data_PERF[data_PERF$Generation=="G0",],
aes(x = Test_environment, y = Nb_eggs, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF,
aes(x = Nb_eggs, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
2.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[4,2])/(1/anova(m1)[4, 1]))## [1] 1.971666(pvalue <- 1 - pf(Fratio, 1, anova(m1)[4, 1])) ## [1] 0.2548912#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[5,2])/(1/anova(m2)[5, 1]))## [1] 2.264412(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[5, 1]) )## [1] 0.2294357## F test for SA
(Fratio_NonGen <- (anova(m2)[4,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 1.369663(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[6, 1]) )## [1] 0.3263818## Compute R2 for SA
## Compute R2 = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(rsqgen <- 1-anova(m2)[5, 3]/((anova(m2)[3, 2]+anova(m2)[5, 2])/(anova(m2)[3, 1]+anova(m2)[5, 1])))## [1] 0.240181(rsqng <- 1-anova(m2)[6, 3]/((anova(m2)[4, 2]+anova(m2)[6, 2])/(anova(m2)[4, 1]+anova(m2)[6, 1])))## [1] 0.08459751#######################################################
## Extract SA value ###
#######################################################
#######################################################
## Extract SA value ###
#######################################################
#Model used previously
m2 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF)
summary(m2)## Df Sum Sq Mean Sq F value
## pop_gen 48 1840.2 38.34 79.495
## hab_gen 4 15.6 3.90 8.085
## SA 1 2.7 2.73 5.655
## SA:IndicG0 1 1.0 0.99 2.054
## Original_environment:Test_environment 3 3.6 1.20 2.497
## IndicG0:Original_environment:Test_environment 3 2.2 0.72 1.499
## Residuals 978 471.7 0.48
## Pr(>F)
## pop_gen < 2e-16 ***
## hab_gen 2.05e-06 ***
## SA 0.0176 *
## SA:IndicG0 0.1522
## Original_environment:Test_environment 0.0584 .
## IndicG0:Original_environment:Test_environment 0.2132
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Model with pophab interaction to have corret SA estimates
m3 <- lm(log(Nb_eggs+1)~ pop_gen + hab_gen + SA*IndicG0 + SA ,
data = data_PERF)
#Estimates SA
cf <-coef(summary(m3,complete = TRUE))
indic <- grep("SA",rownames(cf))
SAcoef <- cf[indic,1]
#names(SAcoef) <- c("SAGen", "SANonGen")
SAcoef## SA1 SA1:IndicG0
## -0.0575654 -0.1411427#Estimates se
indic <- grep("SA",rownames(vcov(m3)))
seSAcoef <- sqrt(diag(vcov(m3)[indic,indic]))
#names(SAcoef) <- c("seSAGen", "seSANonGen")
seSAcoef## SA1 SA1:IndicG0
## 0.06306995 0.09879136##Plot
(PLOT_eggs_G0 <- plot_RTP_residuals(dataset = data_PERF, trait = "Nb_eggs", gen = "G0"))
(PLOT_eggs_G2 <- plot_RTP_residuals(dataset = data_PERF, trait = "Nb_eggs", gen = "G2"))
(PLOT_GEN_eggs_G0 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF,
trait = "Nb_eggs",
effect = "Non-genetic"))
(PLOT_GEN_eggs_G2 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF,
trait = "Nb_eggs",
effect = "Genetic"))
3 NUMBER OF ADULTS
3.1 Exploration data
tapply(data_PERF$Nb_adults, list(data_PERF$Original_environment, data_PERF$Test_environment, data_PERF$Generation), mean, na.rm = TRUE)## , , G0
##
## Blackberry Cherry Strawberry
## Blackberry 4.508197 4.457627 3.816327
## Cherry 6.873418 5.675000 2.378378
## Strawberry 12.230769 16.000000 5.416667
##
## , , G2
##
## Blackberry Cherry Strawberry
## Blackberry 27.31776 23.18868 13.15888
## Cherry 22.91304 23.32000 11.53191
## Strawberry 25.65217 22.36170 16.04348ggplot2::ggplot(data = data_PERF[data_PERF$Generation=="G0",],
aes(x = Test_environment, y = Nb_adults, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF[data_PERF$Generation=="G2",],
aes(x = Test_environment, y = Nb_adults, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF,
aes(x = Nb_adults, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
3.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[4,2])/(1/anova(m1)[4, 1]))## [1] 2.01345(pvalue <- 1 - pf(Fratio, 1, anova(m1)[4, 1])) ## [1] 0.2509626#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[5,2])/(1/anova(m2)[5, 1]))## [1] 2.447544(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[5, 1]) )## [1] 0.2156678## F test for SA
(Fratio_NonGen <- (anova(m2)[4,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 1.111569(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[6, 1]) )## [1] 0.3691494## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[5, 3]/((anova(m2)[3, 2]+anova(m2)[5, 2])/(anova(m2)[3, 1]+anova(m2)[5, 1]))))## [1] 0.2657241(r2_SA_nongenet <- 1-(anova(m2)[6, 3]/((anova(m2)[4, 2]+anova(m2)[6, 2])/(anova(m2)[4, 1]+anova(m2)[6, 1]))))## [1] 0.027135413.3 Analysis with eggs as covariable
#######################################################
## Should we consider the number of eggs? ###
#######################################################
# Original
m1 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF)
## Correction for number of eggs
m2 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs+1),
data = data_PERF)
## With egg score
m3 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScore,
data = data_PERF)
## Compare with 5 egg scores
m4 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScoreFive,
data = data_PERF)
## Compare with EggScoreSmall
m5 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScoreSmall,
data = data_PERF)
MuMIn::model.sel(m1, m2, m3, m4, m5)## Model selection table
## (Int) hab_gen pop_gen SA IG0:SA Org_env:Tst_env IG0:Org_env:Tst_env
## m2 -0.06648 + + + + + +
## m5 0.41840 + + + + + +
## m3 0.42840 + + + + + +
## m4 0.44140 + + + + + +
## m1 0.62560 + + + + + +
## log(Nb_egg+1) EgS ESF ESS family df logLik AICc delta
## m2 0.5125 gaussian(identity) 63 -977.876 2090.0 0.00
## m5 + gaussian(identity) 67 -1040.074 2223.5 133.51
## m3 + gaussian(identity) 65 -1045.327 2229.5 139.45
## m4 + gaussian(identity) 66 -1044.861 2230.8 140.80
## m1 gaussian(identity) 62 -1118.184 2368.4 278.35
## weight
## m2 1
## m5 0
## m3 0
## m4 0
## m1 0
## Models ranked by AICc(x)# Models are not all fitted to the same data: because 6 tubes without Nb_eggs are missing for m2, m3, m4 and m5
## Cl= The best model is when the number of eggs is considered as a continuous variable
### model m2 provides a better description of the data than model m1
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment + log(Nb_eggs+1),
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[5,2])/(1/anova(m1)[5, 1]))## [1] 12.95037(pvalue <- 1 - pf(Fratio, 1, anova(m1)[5, 1])) ## [1] 0.03679697#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs+1),
data = data_PERF)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 17.09331(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[6, 1]) )## [1] 0.02567856## F test for SA
(Fratio_NonGen <- (anova(m2)[5,2]/anova(m2)[7,2])/(1/anova(m2)[7, 1]))## [1] 0.6257893(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[7, 1]) )## [1] 0.4866763## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))))## [1] 0.8009287(r2_SA_nongenet <- 1-(anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))))## [1] -0.1032081##Plot
(PLOT_adult_G0 <- plot_RTP_residuals(dataset = data_PERF, trait = "Nb_adults", gen = "G0"))
(PLOT_adult_G2 <- plot_RTP_residuals(dataset = data_PERF, trait = "Nb_adults", gen = "G2"))
(PLOT_GEN_adult_G0 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF,
trait = "Nb_adults",
effect = "Non-genetic"))
(PLOT_GEN_adult_G2 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF,
trait = "Nb_adults",
effect = "Genetic"))
4 OFFPSRING PERFORMANCE
4.1 Exploration data
tapply(data_PERF_Rate$Rate, list(data_PERF_Rate$Original_environment,
data_PERF_Rate$Test_environment,
data_PERF_Rate$Generation), mean, na.rm = TRUE)## , , G0
##
## Blackberry Cherry Strawberry
## Blackberry 0.6951502 0.5960536 0.6508990
## Cherry 0.2708679 0.3454487 0.2493662
## Strawberry 0.3435592 0.3595403 0.3373855
##
## , , G2
##
## Blackberry Cherry Strawberry
## Blackberry 0.2648444 0.1923140 0.1345134
## Cherry 0.1855892 0.1801664 0.1103531
## Strawberry 0.2206513 0.1950778 0.1619433ggplot2::ggplot(data = data_PERF_Rate[data_PERF_Rate$Generation=="G0",],
aes(x = Test_environment, y = Rate, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF_Rate[data_PERF_Rate$Generation=="G2",],
aes(x = Test_environment, y = Rate, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF_Rate,
aes(x = Rate, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
lattice::xyplot(Rate~Nb_eggs|Original_environment*Test_environment,
data=data_PERF_Rate)
lattice::xyplot(Rate~EggScore|Original_environment*Test_environment,
data=data_PERF_Rate)
lattice::bwplot(Rate~EggScore|Original_environment*Test_environment,
data=data_PERF_Rate)
lattice::bwplot(Rate~EggScoreFive|Original_environment*Test_environment,
data=data_PERF_Rate)
lattice::bwplot(Rate~EggScoreSmall|Original_environment*Test_environment,
data=data_PERF_Rate)
AvEmergenceRate <- tapply(data_PERF_Rate$Rate,
list(data_PERF_Rate$EggScoreFive,
data_PERF_Rate$Original_environment,
data_PERF_Rate$Test_environment),mean)
tapply(data_PERF_Rate$Rate, list(data_PERF_Rate$EggScoreFive,
data_PERF_Rate$Original_environment,
data_PERF_Rate$Test_environment), length)## , , Blackberry
##
## Blackberry Cherry Strawberry
## 1 71 72 14
## 2 43 22 15
## 3 41 17 20
## 4 10 10 8
## 5 3 4 2
##
## , , Cherry
##
## Blackberry Cherry Strawberry
## 1 59 73 8
## 2 31 18 19
## 3 50 21 21
## 4 23 11 9
## 5 2 7 NA
##
## , , Strawberry
##
## Blackberry Cherry Strawberry
## 1 55 75 18
## 2 55 24 13
## 3 39 16 16
## 4 7 6 9
## 5 NA NA 2AvEmergenceRate[, , "Blackberry"][, "Cherry"]/AvEmergenceRate[, , "Blackberry"][, "Blackberry"]## 1 2 3 4 5
## 0.4110203 0.9581939 0.7334304 0.9735309 0.1709919AvEmergenceRate[, , "Cherry"][, "Blackberry"]/AvEmergenceRate[, , "Cherry"][, "Cherry"]## 1 2 3 4 5
## 1.6961620 0.8562518 1.0861981 0.8514873 1.4918117AvEmergenceRate[, , "Blackberry"][, "Cherry"]/AvEmergenceRate[, , "Cherry"][, "Cherry"]## 1 2 3 4 5
## 0.7561587 1.0522488 1.0774054 0.9037047 0.46513154.2 Analysis without eggs
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF_Rate)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[4,2])/(1/anova(m1)[4, 1]))## [1] 20.07511(pvalue <- 1 - pf(Fratio, 1, anova(m1)[4, 1])) ## [1] 0.02073057#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF_Rate)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[5,2])/(1/anova(m2)[5, 1]))## [1] 30.92757(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[5, 1]) )## [1] 0.01147033## F test for SA
(Fratio_NonGen <- (anova(m2)[4,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 1.505315(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[6, 1]) )## [1] 0.3073606## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[5, 3]/((anova(m2)[3, 2]+anova(m2)[5, 2])/(anova(m2)[3, 1]+anova(m2)[5, 1]))))## [1] 0.8821018(r2_SA_nongenet <- 1-(anova(m2)[6, 3]/((anova(m2)[4, 2]+anova(m2)[6, 2])/(anova(m2)[4, 1]+anova(m2)[6, 1]))))## [1] 0.11215974.3 Variance of eggs as covariable
#Goal estimate:
# variance across tubes
# variance across pop
# variance among host
#### Mixte model
lm_vareggs <- lme4::lmer(log(Nb_eggs) ~ 1 + (1|Population) + (1|Test_environment),
data = data_PERF_Rate)
summary(lm_vareggs)## Linear mixed model fit by REML ['lmerMod']
## Formula: log(Nb_eggs) ~ 1 + (1 | Population) + (1 | Test_environment)
## Data: data_PERF_Rate
##
## REML criterion at convergence: 3855.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8453 -0.5829 0.2357 0.7148 2.3291
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population (Intercept) 0.6633 0.8144
## Test_environment (Intercept) 0.0159 0.1261
## Residual 2.2475 1.4992
## Number of obs: 1039, groups: Population, 25; Test_environment, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.3948 0.1856 18.29(VAR_TESTENVT<-as.data.frame(lme4::VarCorr(lm_vareggs))$vcov[2])## [1] 0.0159008(VAR_POPULATION<-as.data.frame(lme4::VarCorr(lm_vareggs))$vcov[1])## [1] 0.6633248(VAR_TUBE<-as.data.frame(lme4::VarCorr(lm_vareggs))$vcov[3])## [1] 2.247517# Var host fruit: 0.01571
# Var population: 0.55243
# Var among tubes: 1.97954
## Extract variance components
VAR <- as.data.frame(lme4::VarCorr(lm_vareggs))$vcov
## Proportion of total variance due to variation among populations
(PropPop <- VAR[1]/sum(VAR))## [1] 0.2266427## Proportion of total variance due to variation among fruits
(PropObs <- VAR[2]/sum(VAR))## [1] 0.005432936## Proportion of total variance due to within tube variance
(PropVarRes <- VAR[3]/sum(VAR)) ## [1] 0.7679244## Cl: High variance among tubes (78% of total variance)
## Cl: Substantial variation among popupation (22% of total variance)
## Cl: Low variance among hosts (>1% of total variance)
## ANOVA
## Fit model with ANOVA
fit0 <- aov(log(Nb_eggs) ~ Population + Test_environment, data = data_PERF_Rate)
anova(fit0)## Analysis of Variance Table
##
## Response: log(Nb_eggs)
## Df Sum Sq Mean Sq F value Pr(>F)
## Population 24 611.54 25.4810 11.3416 < 2e-16 ***
## Test_environment 2 15.91 7.9528 3.5398 0.02938 *
## Residuals 1012 2273.65 2.2467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Variance among fruits
(var_fruit <- (anova(fit0)[2,3]-anova(fit0)[3,3])/mean(tapply(data_PERF_Rate$Nb_eggs,
data_PERF_Rate$Test_environment,length)))## [1] 0.01647582#Variance among populations
(var_pop <- (anova(fit0)[1,3]-anova(fit0)[3,3])/mean(tapply(data_PERF_Rate$Nb_eggs,
data_PERF_Rate$Population,length)))## [1] 0.5590553# Within group variance component = Residual variance
(var_tube <- (anova(fit0))[3,3])## [1] 2.246687sigma(fit0)^2 # the two match## [1] 2.246687#Plot
mod1 <- lm(asin(sqrt(Rate)) ~ log(Nb_eggs) * Test_environment, data = data_PERF_Rate)
#Predict data
filldata <- data.frame(Nb_eggs = rep(seq(min(data_PERF_Rate$Nb_eggs),max(data_PERF_Rate$Nb_eggs)),3),
Test_environment = rep(levels(data_PERF_Rate$Test_environment),
each = length(seq(min(data_PERF_Rate$Nb_eggs),
max(data_PERF_Rate$Nb_eggs)))),
Estimates = NA)
filldata$Estimate_transformed <- predict(mod1, newdata=filldata, re.form=~0)
filldata$Estimates <- (sin(filldata$Estimate_transformed))^2
#If y=arcsin(sqrt(x)) then x=(sin(y))^2.
Plot_eggs <- ggplot2::ggplot(data = data_PERF_Rate,
aes(x = Nb_eggs, y = Rate, color = Test_environment)) +
geom_point(size = 0.7) +
geom_line(data = filldata, aes(x = Nb_eggs,
y = Estimates,
colour = Test_environment), size = 0.9) +
scale_color_manual(name="Test fruit",
breaks=c("Blackberry","Cherry","Strawberry"),
labels=c("Blackberry","Cherry","Strawberry"),
values=c("#301934","#BC3C6D", "#3FAA96"),
drop=FALSE) +
ylab("Offspring performance") +
xlab("Number of eggs") +
theme_LO_sober
cowplot::save_plot(file =here::here("figures", "FigSX_DensityEggs_Effect.pdf"),
Plot_eggs,
base_height = 12/cm(1), base_width = 14/cm(1), dpi = 610)
######## Disucssion avec Nico
var(log((data_PERF_Rate$Nb_adults+1)^2))## [1] 6.260252var(log((data_PERF_Rate$Nb_eggs+1)))## [1] 2.251453var(log((data_PERF_Rate$Nb_adults+1)))## [1] 1.565063var(log((data_PERF_Rate$Nb_eggs+1)))## [1] 2.251453var(log((data_PERF$Nb_adults)))## [1] NaNvar(log((data_PERF$Nb_eggs)))## [1] 2.794891#The coefficient of variation (CV) is a statistical measure of the relative dispersion of data points in a data series around the mean.
sd(data_PERF_Rate$Nb_adults[data_PERF_Rate$Generation=="G0"])/mean(data_PERF_Rate$Nb_adults[data_PERF_Rate$Generation=="G0"])## [1] 1.768041sd(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"])/mean(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"])## [1] 1.376967sd(data_PERF_Rate$Nb_adults[data_PERF_Rate$Generation=="G2"])/mean(data_PERF_Rate$Nb_adults[data_PERF_Rate$Generation=="G2"])## [1] 0.821511sd(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G2"])/mean(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G2"])## [1] 0.385294#
#
# sd(asin(sqrt(data_PERF_Rate$Rate[data_PERF_Rate$Generation=="G0"])))/mean(asin(sqrt(data_PERF_Rate$Rate[data_PERF_Rate$Generation=="G0"])))
# sd(asin(sqrt(data_PERF_Rate$Rate[data_PERF_Rate$Generation=="G2"])))/mean(asin(sqrt(data_PERF_Rate$Rate[data_PERF_Rate$Generation=="G2"])))
#
#
# sd(log(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"]))/mean(log(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"]))
# sd(log(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"]))/mean(log(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"]))
#
# sd(data_PERF_Rate$Rate[data_PERF_Rate$Generation=="G0"])/mean(data_PERF_Rate$Rate[data_PERF_Rate$Generation=="G0"])
# sd(data_PERF_Rate$Rate[data_PERF_Rate$Generation=="G2"])/mean(data_PERF_Rate$Rate[data_PERF_Rate$Generation=="G2"])
#
#
# sd(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"])/mean(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"])
# sd(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"])/mean(data_PERF_Rate$Nb_eggs[data_PERF_Rate$Generation=="G0"])
sd(data_PERF_Rate$Rate)/mean(data_PERF_Rate$Rate)## [1] 1.01659sd(data_PERF_Rate$Nb_eggs)/mean(data_PERF_Rate$Nb_eggs)## [1] 0.83962814.4 Analysis with eggs as covariable
#######################################################
## Should we consider the number of eggs? ###
#######################################################
# Original
m1 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF_Rate)
## Correction for number of eggs
m2 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs),
data = data_PERF_Rate)
## With egg score
m3 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScore,
data = data_PERF_Rate)
## Compare with 5 egg scores
m4 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScoreFive,
data = data_PERF_Rate)
## Compare with EggScoreSmall
m5 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScoreSmall,
data = data_PERF_Rate)
## Correction for number of eggs
m6 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
Nb_eggs,
data = data_PERF_Rate)
data_PERF_Rate$Square_NbEggs <- data_PERF_Rate$Nb_eggs^2
## Correction for number of eggs
m7 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
Nb_eggs + Square_NbEggs,
data = data_PERF_Rate)
## Correction for number of eggs
m8 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
Square_NbEggs,
data = data_PERF_Rate)
MuMIn::model.sel(m1, m2, m3, m4, m5, m6, m7, m8)## Model selection table
## (Int) hab_gen pop_gen SA IG0:SA Org_env:Tst_env IG0:Org_env:Tst_env
## m2 0.8300 + + + + + +
## m7 0.7880 + + + + + +
## m6 0.7427 + + + + + +
## m8 0.7305 + + + + + +
## m1 0.7400 + + + + + +
## m3 0.7471 + + + + + +
## m4 0.7460 + + + + + +
## m5 0.7474 + + + + + +
## log(Nb_egg) EgS ESF ESS Nb_egg Sqr_NbE family df logLik
## m2 -0.1041 gaussian(identity) 63 -217.514
## m7 -0.004514 1.424e-05 gaussian(identity) 64 -239.190
## m6 -0.001382 gaussian(identity) 63 -246.327
## m8 -3.453e-06 gaussian(identity) 63 -251.902
## m1 gaussian(identity) 62 -254.885
## m3 + gaussian(identity) 65 -252.195
## m4 + gaussian(identity) 66 -252.181
## m5 + gaussian(identity) 67 -251.779
## AICc delta weight
## m2 569.3 0.00 1
## m7 614.9 45.62 0
## m6 626.9 57.63 0
## m8 638.1 68.78 0
## m1 641.8 72.48 0
## m3 643.2 73.91 0
## m4 645.5 76.16 0
## m5 646.9 77.64 0
## Models ranked by AICc(x)## Cl= The best model is when the number of eggs is considered as a continuous variable
### model m2 provides a better description of the data than model m1
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment +
log(Nb_eggs),
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF_Rate)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[5,2])/(1/anova(m1)[5, 1]))## [1] 16.94926(pvalue <- 1 - pf(Fratio, 1, anova(m1)[5, 1])) ## [1] 0.02596669## Compute R2 = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(rsqgen <- 1-anova(m1)[5, 3]/((anova(m1)[3, 2]+anova(m1)[4, 2])/(anova(m1)[3, 1]+anova(m1)[4, 1])))## [1] 0.9842831#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs),
data = data_PERF_Rate)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 18.92885(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[6, 1]) )## [1] 0.02242804## F test for SA
(Fratio_NonGen <- (anova(m2)[5,2]/anova(m2)[7,2])/(1/anova(m2)[7, 1]))## [1] 0.7229752(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[7, 1]) )## [1] 0.4576467# Compute R2 = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
rsqgen <- 1-anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))
rsqng <- 1-anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))
## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))))## [1] 0.8175919(r2_SA_nongenet <- 1-(anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))))## [1] -0.07440953#######################################################
## Extract SA value ###
#######################################################
#Model used previously
m2 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs),
data = data_PERF_Rate)
summary(m2)## Df Sum Sq Mean Sq F value
## pop_gen 48 83.07 1.731 18.286
## hab_gen 4 2.24 0.561 5.925
## SA 1 1.06 1.062 11.222
## log(Nb_eggs) 1 6.91 6.912 73.030
## SA:IndicG0 1 0.08 0.078 0.820
## Original_environment:Test_environment 3 0.17 0.056 0.593
## IndicG0:Original_environment:Test_environment 3 0.32 0.107 1.134
## Residuals 977 92.47 0.095
## Pr(>F)
## pop_gen < 2e-16 ***
## hab_gen 0.000103 ***
## SA 0.000839 ***
## log(Nb_eggs) < 2e-16 ***
## SA:IndicG0 0.365390
## Original_environment:Test_environment 0.619771
## IndicG0:Original_environment:Test_environment 0.334151
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Model with pophab interaction to have corret SA estimates
m3 <- lm(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA*IndicG0 + SA + log(Nb_eggs),
data = data_PERF_Rate)
#Estimates SA
cf <-coef(summary(m3,complete = TRUE))
indic <- grep("SA",rownames(cf))
SAcoef <- cf[indic,1]
#names(SAcoef) <- c("SAGen", "SANonGen")
SAcoef## SA1 SA1:IndicG0
## 0.04226139 0.03955350#Estimates se
indic <- grep("SA",rownames(vcov(m3)))
seSAcoef <- sqrt(diag(vcov(m3)[indic,indic]))
#names(SAcoef) <- c("seSAGen", "seSANonGen")
seSAcoef## SA1 SA1:IndicG0
## 0.02785344 0.043660134.5 Heterogeneity across populations
data_Rate_G2 <- data_PERF_Rate[data_PERF_Rate$Generation=="G2",]
data_Rate_G2 <- data_Rate_G2[complete.cases(data_Rate_G2$Rate), ]
m0 <- lm(Rate~Original_environment*Test_environment, data=data_Rate_G2,)
summary(m0)##
## Call:
## lm(formula = Rate ~ Original_environment * Test_environment,
## data = data_Rate_G2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.26484 -0.09978 -0.02467 0.07237 0.73516
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.26484 0.01330
## Original_environmentCherry -0.07926 0.02426
## Original_environmentStrawberry -0.04419 0.02426
## Test_environmentCherry -0.07253 0.01886
## Test_environmentStrawberry -0.13033 0.01881
## Original_environmentCherry:Test_environmentCherry 0.06711 0.03385
## Original_environmentStrawberry:Test_environmentCherry 0.04696 0.03421
## Original_environmentCherry:Test_environmentStrawberry 0.05509 0.03418
## Original_environmentStrawberry:Test_environmentStrawberry 0.07162 0.03431
## t value Pr(>|t|)
## (Intercept) 19.909 < 2e-16 ***
## Original_environmentCherry -3.267 0.001150 **
## Original_environmentStrawberry -1.822 0.069020 .
## Test_environmentCherry -3.846 0.000133 ***
## Test_environmentStrawberry -6.928 1.12e-11 ***
## Original_environmentCherry:Test_environmentCherry 1.982 0.047891 *
## Original_environmentStrawberry:Test_environmentCherry 1.373 0.170346
## Original_environmentCherry:Test_environmentStrawberry 1.612 0.107534
## Original_environmentStrawberry:Test_environmentStrawberry 2.088 0.037265 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1376 on 593 degrees of freedom
## Multiple R-squared: 0.1044, Adjusted R-squared: 0.09231
## F-statistic: 8.64 on 8 and 593 DF, p-value: 3.499e-11tapply(data_Rate_G2$Rate,
list(data_Rate_G2$Original_environment,
data_Rate_G2$Test_environment), mean)## Blackberry Cherry Strawberry
## Blackberry 0.2648444 0.1923140 0.1345134
## Cherry 0.1855892 0.1801664 0.1103531
## Strawberry 0.2206513 0.1950778 0.1619433tapply(data_Rate_G2$Rate,
list(data_Rate_G2$Original_environment,
data_Rate_G2$Test_environment), var)## Blackberry Cherry Strawberry
## Blackberry 0.03491514 0.013749525 0.01436498
## Cherry 0.02922031 0.019467766 0.01399160
## Strawberry 0.01555929 0.009464032 0.01139614range(data_Rate_G2$Nb_adults, na.rm = TRUE)## [1] 0 108## Check number of eggs and adults
tapply(data_Rate_G2$Nb_eggs,
list(data_Rate_G2$Original_environment,
data_Rate_G2$Test_environment), mean)## Blackberry Cherry Strawberry
## Blackberry 103.9720 121.8491 98.71963
## Cherry 131.7174 133.8400 103.51064
## Strawberry 119.1739 119.4894 111.56522tapply(data_Rate_G2$Nb_adults,
list(data_Rate_G2$Original_environment,
data_Rate_G2$Test_environment), mean)## Blackberry Cherry Strawberry
## Blackberry 27.31776 23.18868 13.15888
## Cherry 22.91304 23.32000 11.53191
## Strawberry 25.65217 22.36170 16.04348## Check for the presence of negative correlations
m1 <- lm(asin(sqrt(Rate)) ~ Population + Test_environment,
data=data_Rate_G2)
data_Rate_G2$resid <- residuals(m1)
meanbypopbytestenv <- as.data.frame(tapply(data_Rate_G2$resid,
list(data_Rate_G2$Population,
data_Rate_G2$Test_environment), mean))
## Cherry ~ Blackberry
plot(meanbypopbytestenv$Blackberry,
meanbypopbytestenv$Cherry,
col=as.numeric(data_Rate_G2$Original_environment[match(rownames(meanbypopbytestenv),
data_Rate_G2$Population)]),
xlab="Blackberry", ylab="Cherry", pch=16)
legend("topright", levels(data_Rate_G2$Original_environment),
col=as.numeric(as.factor(levels(data_Rate_G2$Original_environment))), pch=16)
## Strawberry ~ Cherry
plot(meanbypopbytestenv$Cherry, meanbypopbytestenv$Strawberry, col=as.numeric(data_Rate_G2$Original_environment[match(rownames(meanbypopbytestenv), data_Rate_G2$Population)]), xlab="Cherry", ylab="Strawberry", pch=16)
legend("bottomright", levels(data_Rate_G2$Original_environment), col=as.numeric(as.factor(levels(data_Rate_G2$Original_environment))), pch=16)
## Strawberry ~ Blackberry
plot(meanbypopbytestenv$Blackberry, meanbypopbytestenv$Strawberry, col=as.numeric(data_Rate_G2$Original_environment[match(rownames(meanbypopbytestenv), data_Rate_G2$Population)]), xlab="Blackberry", ylab="Strawberry", pch=16)
legend("topright", levels(data_Rate_G2$Original_environment), col=as.numeric(as.factor(levels(data_Rate_G2$Original_environment))), pch=16)
##Plot
(PLOT_rate_G0 <- plot_RTP_residuals(dataset = data_PERF_Rate, trait = "Rate", gen = "G0"))
(PLOT_rate_G2 <- plot_RTP_residuals(dataset = data_PERF_Rate, trait = "Rate", gen = "G2"))
(PLOT_GEN_rate_G0 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF_Rate,
trait = "Rate",
effect = "Non-genetic"))
(PLOT_GEN_rate_G2 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF_Rate,
trait = "Rate",
effect = "Genetic"))
5 OVIOSITION PREFERENCE
5.1 Twelve fruits
5.1.1 Exploration data
tapply(data_PREF$Nb_eggs, list(data_PREF$Original_environment,
data_PREF$Test_environment,
data_PREF$Generation), mean, na.rm = TRUE)## , , G0
##
## Apricot Blackberry Blackcurrant Cherry Cranberry Fig
## Blackberry 0.2758621 0.7931034 0.1379310 0.4137931 0.2068966 0.2586207
## Cherry 0.3300000 0.5000000 1.2600000 2.4800000 0.3800000 0.7400000
## Strawberry 0.5714286 1.1904762 0.6666667 1.6666667 0.5714286 0.4285714
## Grape Kiwi Raspberry Rosehips Strawberry Tomato
## Blackberry 0.2068966 0.0862069 0.4655172 0.3793103 0.5862069 0.2241379
## Cherry 0.9700000 1.0500000 1.5800000 1.2400000 0.6700000 0.6969697
## Strawberry 0.4761905 0.1428571 0.6666667 0.3809524 0.8571429 0.0952381
##
## , , G2
##
## Apricot Blackberry Blackcurrant Cherry Cranberry Fig
## Blackberry 6.826531 24.62245 18.61224 23.41837 8.428571 12.73469
## Cherry 10.423077 16.09615 19.09615 30.38462 7.480769 13.46154
## Strawberry 15.450000 21.92500 22.12500 24.92500 7.200000 13.02500
## Grape Kiwi Raspberry Rosehips Strawberry Tomato
## Blackberry 24.55102 16.57143 16.59184 11.23469 6.938776 12.53061
## Cherry 11.67308 11.90385 12.78846 11.46154 10.673077 10.59615
## Strawberry 12.55000 14.87500 19.45000 16.20000 12.775000 14.90000ggplot2::ggplot(data = data_PREF,
aes(x = Test_environment, y = Nb_eggs, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PREF,
aes(x = Nb_eggs, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
5.1.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PREF)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[4,2])/(1/anova(m1)[4, 1]))## [1] 10.1742(pvalue <- 1 - pf(Fratio, 1, anova(m1)[4, 1])) ## [1] 0.004407544#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
BoxID,
data = data_PREF)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 9.833047(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[6, 1]) )## [1] 0.004993505## F test for SA
(Fratio_NonGen <- (anova(m2)[5,2]/anova(m2)[7,2])/(1/anova(m2)[7, 1]))## [1] 2.500166(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[7, 1]) )## [1] 0.1287796## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))))## [1] 0.2864799(r2_SA_nongenet <- 1-(anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))))## [1] 0.0638364#Local adaptation pattern:
lm_val = lm(log(Nb_eggs+1) ~ Test_environment + Population + SA +
Test_environment:Original_environment + BoxID,
data = data_PREF[data_PREF$Generation=="G0",])
(Fratio = anova(lm_val)[3,3]/anova(lm_val)[5,3])## [1] 4.476081(pvalue = 1 - pf(Fratio,anova(lm_val)[3,1],anova(lm_val)[5,1]))## [1] 0.0464917(df1 = anova(lm_val)[3,1])## [1] 1(df2 = anova(lm_val)[5,1])## [1] 21lm_val = lm(log(Nb_eggs+1) ~ Test_environment + Population + SA +
Test_environment:Original_environment + BoxID,
data = data_PREF[data_PREF$Generation=="G2",])
(Fratio = anova(lm_val)[3,3]/anova(lm_val)[5,3])## [1] 6.319972(pvalue = 1 - pf(Fratio,anova(lm_val)[3,1],anova(lm_val)[5,1]))## [1] 0.02016054(df1 = anova(lm_val)[3,1])## [1] 1(df2 = anova(lm_val)[5,1])## [1] 215.2 Three fruits
5.2.1 Exploration data
tapply(data_PREF_three$Nb_eggs, list(data_PREF_three$Original_environment,
data_PREF_three$Test_environment,
data_PREF_three$Generation), mean, na.rm = TRUE)## , , G0
##
## Blackberry Cherry Strawberry
## Blackberry 0.7931034 0.4137931 0.5862069
## Cherry 0.5000000 2.4800000 0.6700000
## Strawberry 1.1904762 1.6666667 0.8571429
##
## , , G2
##
## Blackberry Cherry Strawberry
## Blackberry 24.62245 23.41837 6.938776
## Cherry 16.09615 30.38462 10.673077
## Strawberry 21.92500 24.92500 12.775000ggplot2::ggplot(data = data_PREF_three,
aes(x = Test_environment, y = Nb_eggs, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PREF_three,
aes(x = Nb_eggs, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
5.2.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment +
BoxID,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PREF_three)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[5,2])/(1/anova(m1)[5, 1]))## [1] 18.38385(pvalue <- 1 - pf(Fratio, 1, anova(m1)[5, 1])) ## [1] 0.02331823#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
BoxID,
data = data_PREF_three)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 12.77418(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[6, 1]) )## [1] 0.0374429## F test for SA
(Fratio_NonGen <- (anova(m2)[5,2]/anova(m2)[7,2])/(1/anova(m2)[7, 1]))## [1] 3.536493(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[7, 1]) )## [1] 0.1566089## Compute R2 for SA
# ## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
# (r2_SA_genet <- 1-(anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))))
#
# (r2_SA_nongenet <- 1-(anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))))
## Compute R2 = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
rsqgen <- 1-anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))
rsqng <- 1-anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))
rsqgen## [1] 0.746421 rsqng## [1] 0.3880511#######################################################
## Extract SA value ###
#######################################################
#Model used previously
m2 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
BoxID,
data = data_PREF_three)
summary(m2)## Df Sum Sq Mean Sq F value
## pop_gen 46 1243.4 27.03 36.767
## hab_gen 4 135.1 33.78 45.940
## SA 1 14.8 14.82 20.162
## BoxID 322 405.3 1.26 1.712
## SA:IndicG0 1 3.9 3.85 5.237
## Original_environment:Test_environment 3 3.5 1.16 1.578
## IndicG0:Original_environment:Test_environment 3 3.3 1.09 1.481
## Residuals 726 533.8 0.74
## Pr(>F)
## pop_gen < 2e-16 ***
## hab_gen < 2e-16 ***
## SA 8.28e-06 ***
## BoxID 2.34e-09 ***
## SA:IndicG0 0.0224 *
## Original_environment:Test_environment 0.1933
## IndicG0:Original_environment:Test_environment 0.2184
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#but without interaction to have correct estimate of SA
m3 <- lm(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
BoxID,
data = data_PREF_three)
# Pour les SA:
# summary(m3)
# coef(m3)
cf <-coef(summary(m3,complete = TRUE))
indic <- grep("SA",rownames(cf))
SAcoef <- cf[indic,1]
names(SAcoef) <- c("SAGen", "SANonGen")
SAcoef## SAGen SANonGen
## 0.3842848 0.2660122#With Nico
m3 <- lm(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA + BoxID,
data = data_PREF_three)
summary(m3)##
## Call:
## lm(formula = log(Nb_eggs + 1) ~ pop_gen + hab_gen + SA:IndicG0 +
## SA + BoxID, data = data_PREF_three)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7761 -0.3517 -0.0258 0.3229 3.0050
##
## Coefficients: (48 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.585e-01 5.084e-01 -0.705 0.481001
## pop_genBlackberry31_G2 4.903e-01 7.105e-01 0.690 0.490366
## pop_genBlackberry32_G0 2.310e-01 7.016e-01 0.329 0.742017
## pop_genBlackberry32_G2 1.954e+00 7.105e-01 2.750 0.006101 **
## pop_genBlackberry33_G0 3.662e-01 7.016e-01 0.522 0.601869
## pop_genBlackberry33_G2 1.888e+00 7.105e-01 2.657 0.008064 **
## pop_genBlackberry34_G0 2.855e-14 7.016e-01 0.000 1.000000
## pop_genBlackberry34_G2 1.494e+00 7.105e-01 2.103 0.035840 *
## pop_genBlackberry35_G0 7.225e-14 7.016e-01 0.000 1.000000
## pop_genBlackberry35_G2 2.358e+00 7.105e-01 3.319 0.000949 ***
## pop_genBlackberry36_G0 9.635e-01 7.016e-01 1.373 0.170113
## pop_genBlackberry36_G2 3.060e+00 7.105e-01 4.307 1.88e-05 ***
## pop_genBlackberry37_G0 2.282e-14 7.016e-01 0.000 1.000000
## pop_genBlackberry37_G2 2.500e+00 7.105e-01 3.519 0.000461 ***
## pop_genBlackberry38_G0 3.821e-14 7.016e-01 0.000 1.000000
## pop_genBlackberry38_G2 2.787e+00 7.105e-01 3.922 9.60e-05 ***
## pop_genBlackberry39_G0 6.931e-01 7.016e-01 0.988 0.323515
## pop_genBlackberry39_G2 5.380e-01 7.105e-01 0.757 0.449169
## pop_genBlackberry40_G0 1.426e+00 7.016e-01 2.032 0.042534 *
## pop_genBlackberry40_G2 1.075e+00 7.105e-01 1.512 0.130904
## pop_genBlackberry43_G2 1.992e+00 7.105e-01 2.803 0.005189 **
## pop_genBlackberry44_G0 2.310e-01 7.016e-01 0.329 0.742017
## pop_genBlackberry44_G2 3.094e+00 7.105e-01 4.355 1.52e-05 ***
## pop_genBlackberry45_G0 4.858e-14 7.016e-01 0.000 1.000000
## pop_genBlackberry45_G2 1.435e+00 7.105e-01 2.019 0.043829 *
## pop_genCherry103_G0 3.662e-01 7.016e-01 0.522 0.601869
## pop_genCherry103_G2 9.771e-01 7.105e-01 1.375 0.169493
## pop_genCherry104_G0 5.365e-01 7.016e-01 0.765 0.444738
## pop_genCherry104_G2 6.502e-01 7.105e-01 0.915 0.360463
## pop_genCherry3_G0 1.397e+00 7.016e-01 1.990 0.046911 *
## pop_genCherry3_G2 2.620e+00 7.105e-01 3.687 0.000244 ***
## pop_genCherry47_G0 3.609e-14 7.016e-01 0.000 1.000000
## pop_genCherry47_G2 9.744e-02 7.105e-01 0.137 0.890963
## pop_genCherry50_G0 2.310e-01 7.016e-01 0.329 0.742017
## pop_genCherry50_G2 3.247e+00 7.105e-01 4.570 5.72e-06 ***
## pop_genCherry51_G0 3.166e-14 7.016e-01 0.000 1.000000
## pop_genCherry52_G0 2.812e-14 7.016e-01 0.000 1.000000
## pop_genCherry52_G2 1.558e+00 7.105e-01 2.193 0.028631 *
## pop_genCherry6_G0 3.672e-14 7.016e-01 0.000 1.000000
## pop_genCherry6_G2 4.030e+00 7.105e-01 5.671 2.04e-08 ***
## pop_genCherry7_G2 2.138e+00 7.105e-01 3.009 0.002708 **
## pop_genStrawberry42_G0 7.324e-01 7.016e-01 1.044 0.296884
## pop_genStrawberry42_G2 1.096e+00 7.105e-01 1.543 0.123380
## pop_genStrawberry44_G0 2.310e-01 7.016e-01 0.329 0.742017
## pop_genStrawberry44_G2 2.623e+00 7.105e-01 3.692 0.000239 ***
## pop_genStrawberry53_G0 1.134e+00 7.016e-01 1.616 0.106551
## pop_genStrawberry53_G2 1.653e+00 7.105e-01 2.327 0.020232 *
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## BoxID1063 -3.264e-01 7.016e-01 -0.465 0.641869
## BoxID1064 -1.589e+00 7.016e-01 -2.265 0.023810 *
## BoxID1065 8.989e-02 7.016e-01 0.128 0.898093
## BoxID1066 -4.156e-01 7.016e-01 -0.592 0.553794
## BoxID1067 -1.337e+00 7.016e-01 -1.905 0.057181 .
## BoxID1068 -3.262e-01 7.016e-01 -0.465 0.642136
## BoxID1069 3.136e-02 7.016e-01 0.045 0.964364
## BoxID1070 2.850e-01 7.016e-01 0.406 0.684739
## BoxID1071 NA NA NA NA
## BoxID1072 -9.217e-01 7.016e-01 -1.314 0.189349
## BoxID1073 -5.917e-01 7.016e-01 -0.843 0.399282
## BoxID1074 -1.286e-01 7.016e-01 -0.183 0.854672
## BoxID1075 -5.813e-01 7.016e-01 -0.829 0.407616
## BoxID1076 -5.014e-01 7.016e-01 -0.715 0.475100
## BoxID1078 -4.577e-01 7.016e-01 -0.652 0.514397
## BoxID1079 -2.097e+00 7.016e-01 -2.989 0.002892 **
## BoxID1080 NA NA NA NA
## BoxID1081 -2.840e-01 7.016e-01 -0.405 0.685780
## BoxID1085 6.433e-01 7.016e-01 0.917 0.359505
## BoxID1086 2.721e-01 7.016e-01 0.388 0.698296
## BoxID1087 -3.946e-01 7.016e-01 -0.562 0.574016
## BoxID1088 -8.458e-01 7.016e-01 -1.205 0.228424
## BoxID1089 8.222e-02 7.016e-01 0.117 0.906739
## BoxID1090 NA NA NA NA
## BoxID1091 -2.467e-01 7.016e-01 -0.352 0.725209
## BoxID1092 -5.527e-01 7.016e-01 -0.788 0.431063
## BoxID1093 NA NA NA NA
## BoxID1094 NA NA NA NA
## BoxID1095 -8.258e-01 7.016e-01 -1.177 0.239594
## BoxID1096 -7.406e-01 7.016e-01 -1.056 0.291540
## BoxID1097 -1.886e+00 7.016e-01 -2.688 0.007350 **
## BoxID1098 -7.403e-02 7.016e-01 -0.106 0.916000
## BoxID1101 -2.399e+00 7.016e-01 -3.420 0.000661 ***
## BoxID1102 -1.776e+00 7.016e-01 -2.531 0.011594 *
## BoxID1103 -2.992e-01 7.016e-01 -0.427 0.669868
## BoxID1104 -4.735e-02 7.016e-01 -0.067 0.946216
## BoxID1105 -1.003e+00 7.016e-01 -1.429 0.153303
## BoxID1107 -3.659e-01 7.016e-01 -0.521 0.602219
## BoxID1108 5.870e-01 7.016e-01 0.837 0.403045
## BoxID1109 -1.371e+00 7.016e-01 -1.954 0.051070 .
## BoxID1110 NA NA NA NA
## BoxID1112 2.114e+00 7.016e-01 3.013 0.002672 **
## BoxID1115 -2.300e-02 7.016e-01 -0.033 0.973861
## BoxID1116 1.200e+00 7.016e-01 1.710 0.087656 .
## BoxID1117 -1.778e-01 7.016e-01 -0.253 0.800057
## BoxID1118 -7.438e-02 7.016e-01 -0.106 0.915601
## BoxID1119 1.729e-01 7.016e-01 0.246 0.805384
## BoxID1120 NA NA NA NA
## SA0:IndicG0 2.660e-01 1.165e-01 2.284 0.022683 *
## SA1:IndicG0 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8593 on 732 degrees of freedom
## Multiple R-squared: 0.7693, Adjusted R-squared: 0.6514
## F-statistic: 6.527 on 374 and 732 DF, p-value: < 2.2e-16coef(m3)## (Intercept) pop_genBlackberry31_G2 pop_genBlackberry32_G0
## -3.584823e-01 4.903231e-01 2.310491e-01
## pop_genBlackberry32_G2 pop_genBlackberry33_G0 pop_genBlackberry33_G2
## 1.954188e+00 3.662041e-01 1.887651e+00
## pop_genBlackberry34_G0 pop_genBlackberry34_G2 pop_genBlackberry35_G0
## 2.855444e-14 1.493990e+00 7.224669e-14
## pop_genBlackberry35_G2 pop_genBlackberry36_G0 pop_genBlackberry36_G2
## 2.358169e+00 9.634573e-01 3.060449e+00
## pop_genBlackberry37_G0 pop_genBlackberry37_G2 pop_genBlackberry38_G0
## 2.281848e-14 2.500058e+00 3.821188e-14
## pop_genBlackberry38_G2 pop_genBlackberry39_G0 pop_genBlackberry39_G2
## 2.786906e+00 6.931472e-01 5.380234e-01
## pop_genBlackberry40_G0 pop_genBlackberry40_G2 pop_genBlackberry43_G2
## 1.425555e+00 1.074503e+00 1.991965e+00
## pop_genBlackberry44_G0 pop_genBlackberry44_G2 pop_genBlackberry45_G0
## 2.310491e-01 3.094169e+00 4.857505e-14
## pop_genBlackberry45_G2 pop_genCherry103_G0 pop_genCherry103_G2
## 1.434728e+00 3.662041e-01 9.771239e-01
## pop_genCherry104_G0 pop_genCherry104_G2 pop_genCherry3_G0
## 5.364793e-01 6.501808e-01 1.396552e+00
## pop_genCherry3_G2 pop_genCherry47_G0 pop_genCherry47_G2
## 2.619542e+00 3.609108e-14 9.743814e-02
## pop_genCherry50_G0 pop_genCherry50_G2 pop_genCherry51_G0
## 2.310491e-01 3.247355e+00 3.165834e-14
## pop_genCherry52_G0 pop_genCherry52_G2 pop_genCherry6_G0
## 2.811518e-14 1.558114e+00 3.672278e-14
## pop_genCherry6_G2 pop_genCherry7_G2 pop_genStrawberry42_G0
## 4.029648e+00 2.138269e+00 7.324082e-01
## pop_genStrawberry42_G2 pop_genStrawberry44_G0 pop_genStrawberry44_G2
## 1.096016e+00 2.310491e-01 2.623487e+00
## pop_genStrawberry53_G0 pop_genStrawberry53_G2 hab_genBlackberry_G2
## 1.133732e+00 1.653486e+00 6.558473e-01
## hab_genCherry_G0 hab_genCherry_G2 hab_genStrawberry_G0
## 1.451974e-01 1.129295e+00 1.394028e-02
## hab_genStrawberry_G2 SA1 BoxID557
## NA 3.842848e-01 -2.310491e-01
## BoxID558 BoxID559 BoxID560
## 5.824560e-15 -2.310491e-01 -2.310491e-01
## BoxID561 BoxID562 BoxID563
## -2.310491e-01 1.351550e-01 -2.310491e-01
## BoxID564 BoxID565 BoxID566
## 4.950275e-15 1.086032e+00 -2.310491e-01
## BoxID567 BoxID568 BoxID577
## 6.716343e-01 -2.310491e-01 2.310491e-01
## BoxID578 BoxID579 BoxID580
## 1.483963e-15 1.122104e-15 1.075797e-15
## BoxID581 BoxID582 BoxID583
## 4.620981e-01 1.127183e-15 4.426449e-16
## BoxID584 BoxID585 BoxID586
## 1.627439e-15 2.310491e-01 1.714978e-15
## BoxID587 BoxID588 BoxID589
## 1.228792e-15 2.310491e-01 4.620981e-01
## BoxID590 BoxID591 BoxID592
## 1.645725e-15 1.538026e-15 NA
## BoxID593 BoxID594 BoxID595
## 5.981733e-15 2.310491e-01 5.890445e-15
## BoxID596 BoxID597 BoxID598
## 5.887325e-15 2.310491e-01 5.841222e-15
## BoxID599 BoxID600 BoxID604
## 6.203814e-15 5.972532e-01 2.310491e-01
## BoxID605 BoxID606 BoxID607
## 6.240506e-15 2.310491e-01 2.310491e-01
## BoxID608 BoxID609 BoxID610
## 6.023590e-15 7.325344e-15 NA
## BoxID611 BoxID612 BoxID613
## -1.423320e-15 3.662041e-01 2.310491e-01
## BoxID614 BoxID615 BoxID616
## 4.620981e-01 -1.012361e-15 -1.378161e-15
## BoxID617 BoxID620 BoxID621
## 4.620981e-01 -4.938682e-01 -9.344535e-01
## BoxID622 BoxID623 BoxID624
## -1.165503e+00 -1.165503e+00 -1.396552e+00
## BoxID625 BoxID629 BoxID630
## NA 4.849427e-15 2.310491e-01
## BoxID631 BoxID632 BoxID633
## 4.377214e-15 3.939944e-15 2.310491e-01
## BoxID634 BoxID635 BoxID636
## 3.666058e-15 2.310491e-01 3.662041e-01
## BoxID638 BoxID639 BoxID640
## 4.056532e-15 3.439320e-15 3.434577e-15
## BoxID641 BoxID642 BoxID643
## 2.310491e-01 NA 8.445656e-01
## BoxID644 BoxID650 BoxID653
## 3.877169e-01 NA -1.124065e-15
## BoxID654 BoxID655 BoxID656
## -1.242006e-15 4.620981e-01 -1.198062e-15
## BoxID657 BoxID658 BoxID660
## -1.196362e-15 -1.225848e-15 -1.373831e-15
## BoxID661 BoxID662 BoxID665
## 2.310491e-01 NA 4.184310e-16
## BoxID666 BoxID667 BoxID668
## -2.310491e-01 -2.310491e-01 -2.310491e-01
## BoxID669 BoxID670 BoxID671
## -2.310491e-01 -2.310491e-01 8.338229e-16
## BoxID673 BoxID674 BoxID675
## -2.310491e-01 4.620981e-01 -2.310491e-01
## BoxID676 BoxID677 BoxID678
## -2.310491e-01 NA 1.146806e+00
## BoxID679 BoxID680 BoxID682
## 6.077385e-02 1.297273e+00 6.486367e-01
## BoxID683 BoxID684 BoxID685
## 6.077385e-02 6.077385e-02 9.849701e-01
## BoxID686 BoxID687 BoxID688
## 5.742555e-01 4.620981e-01 -3.054302e-01
## BoxID689 BoxID690 BoxID691
## 2.310491e-01 -3.054302e-01 1.546846e+00
## BoxID692 BoxID693 BoxID694
## 3.185038e-01 3.662041e-01 5.086854e-01
## BoxID695 BoxID696 BoxID775
## 1.018000e+00 NA -7.324082e-01
## BoxID776 BoxID777 BoxID778
## -3.662041e-01 1.702752e-01 NA
## BoxID779 BoxID780 BoxID782
## -7.324082e-01 -1.351550e-01 -5.013591e-01
## BoxID783 BoxID784 BoxID794
## 3.269431e-01 NA 3.662041e-01
## BoxID795 BoxID796 BoxID797
## -1.347526e+00 -1.775618e+00 -3.937386e-01
## BoxID798 BoxID800 BoxID806
## 1.476939e+00 -9.412940e-01 NA
## BoxID814 BoxID815 BoxID816
## -7.472970e-01 -2.964994e-01 -8.298011e-01
## BoxID817 BoxID818 BoxID819
## 5.390953e-03 -4.362145e-01 NA
## BoxID820 BoxID828 BoxID829
## NA -1.623243e+00 -1.003091e-01
## BoxID831 BoxID832 BoxID833
## -2.502929e+00 9.842383e-01 -1.429970e+00
## BoxID834 BoxID835 BoxID836
## -1.623243e+00 -8.463246e-01 2.735498e-02
## BoxID837 BoxID838 BoxID839
## 6.586153e-01 -1.273303e+00 -4.548460e-01
## BoxID840 BoxID841 BoxID842
## -1.202340e-01 -5.598730e-01 -9.302179e-01
## BoxID843 BoxID851 BoxID852
## -1.415314e+00 6.393422e-01 -1.003306e+00
## BoxID853 BoxID854 BoxID855
## 4.543804e-01 1.180388e+00 -8.629011e-01
## BoxID856 BoxID857 BoxID858
## -3.855332e-01 6.129509e-02 -9.587952e-01
## BoxID859 BoxID860 BoxID861
## -4.764888e-01 -1.351550e-01 1.664581e+00
## BoxID862 BoxID863 BoxID864
## 1.002718e+00 1.450630e+00 2.059807e+00
## BoxID865 BoxID866 BoxID867
## 2.271377e+00 2.615921e+00 2.007412e+00
## BoxID869 BoxID870 BoxID871
## NA 4.038965e-01 -9.587952e-01
## BoxID872 BoxID873 BoxID874
## 6.646316e-02 3.518134e-01 5.285613e-01
## BoxID875 BoxID876 BoxID877
## -5.412075e-01 -7.492590e-01 -1.420893e+00
## BoxID878 BoxID881 BoxID882
## -4.728212e-03 -2.310491e-01 -2.310491e-01
## BoxID883 BoxID884 BoxID885
## 2.310491e-01 -3.662041e-01 -3.662041e-01
## BoxID886 BoxID887 BoxID888
## -3.662041e-01 -3.662041e-01 -3.662041e-01
## BoxID889 BoxID890 BoxID891
## -3.662041e-01 7.202856e-01 -6.486367e-01
## BoxID893 BoxID894 BoxID895
## -8.796858e-01 1.564655e+00 8.253127e-01
## BoxID902 BoxID904 BoxID905
## -2.310491e-01 -2.310491e-01 2.310491e-01
## BoxID906 BoxID907 BoxID908
## -2.310491e-01 2.609308e-14 NA
## BoxID910 BoxID911 BoxID912
## 2.909693e-01 3.419921e+00 2.694443e+00
## BoxID913 BoxID914 BoxID915
## -5.966212e-01 NA -1.042869e+00
## BoxID916 BoxID917 BoxID918
## 2.280182e+00 1.300658e+00 1.749008e+00
## BoxID919 BoxID920 BoxID921
## 2.640270e+00 2.751076e+00 1.551320e+00
## BoxID922 BoxID923 BoxID924
## 1.165503e+00 6.825643e-01 9.999452e-01
## BoxID925 BoxID926 BoxID927
## NA 3.128042e-01 -1.735401e+00
## BoxID928 BoxID933 BoxID934
## NA 1.589989e+00 7.336232e-01
## BoxID935 BoxID936 BoxID937
## 1.042775e+00 1.260167e+00 NA
## BoxID939 BoxID943 BoxID944
## NA 1.516116e+00 -1.865386e-01
## BoxID945 BoxID946 BoxID947
## -3.662041e-01 1.021575e+00 NA
## BoxID949 BoxID950 BoxID951
## 5.660778e-01 NA -1.325746e-14
## BoxID952 BoxID953 BoxID954
## 8.283022e-01 -7.636573e-14 6.931472e-01
## BoxID955 BoxID956 BoxID957
## 6.931472e-01 7.415802e-15 -5.305915e-15
## BoxID958 BoxID959 BoxID960
## 2.172013e-14 NA NA
## BoxID962 BoxID963 BoxID965
## 3.127565e-01 NA 1.397662e-15
## BoxID966 BoxID967 BoxID968
## 2.581443e-14 9.258647e-16 1.505527e-15
## BoxID969 BoxID970 BoxID971
## 9.872099e-16 2.202295e-15 1.242385e-15
## BoxID972 BoxID973 BoxID974
## NA 2.679866e-14 2.597101e-14
## BoxID975 BoxID976 BoxID977
## 2.310491e-01 1.229626e+00 2.310491e-01
## BoxID978 BoxID979 BoxID980
## NA 3.662041e-01 7.992984e-01
## BoxID983 BoxID984 BoxID985
## -6.931472e-01 -1.670937e-15 NA
## BoxID986 BoxID987 BoxID988
## 5.364793e-01 5.633805e-15 2.542049e-15
## BoxID989 BoxID990 BoxID991
## NA 1.351550e-01 2.612920e-14
## BoxID992 BoxID993 BoxID994
## NA NA -1.425555e+00
## BoxID997 BoxID998 BoxID999
## NA NA NA
## BoxID1001 BoxID1002 BoxID1003
## 2.293092e+00 3.105316e+00 3.105194e-01
## BoxID1004 BoxID1005 BoxID1006
## 4.620981e-01 1.675974e+00 2.020665e+00
## BoxID1008 BoxID1009 BoxID1010
## 1.338101e+00 1.890225e+00 NA
## BoxID1011 BoxID1012 BoxID1013
## 1.843143e+00 2.305241e+00 4.783615e-01
## BoxID1014 BoxID1015 BoxID1016
## 1.872661e+00 -8.581729e-01 -7.067545e-01
## BoxID1017 BoxID1018 BoxID1019
## -1.259497e+00 -1.586440e+00 -1.072959e+00
## BoxID1020 BoxID1021 BoxID1022
## -2.318848e+00 5.408944e-01 NA
## BoxID1024 BoxID1025 BoxID1026
## -1.257647e-01 4.791165e-01 -1.856750e+00
## BoxID1027 BoxID1028 BoxID1029
## -1.625701e+00 -9.431370e-01 -6.168667e-01
## BoxID1030 BoxID1036 BoxID1037
## NA 1.599639e+00 -2.902761e-01
## BoxID1038 BoxID1039 BoxID1040
## 9.744082e-02 9.219137e-02 5.501486e-01
## BoxID1041 BoxID1042 BoxID1043
## 8.073058e-01 -7.916353e-01 1.321561e+00
## BoxID1047 BoxID1048 BoxID1049
## 1.093229e+00 -1.288854e+00 4.866554e-01
## BoxID1050 BoxID1051 BoxID1052
## NA -8.658211e-01 -1.544473e+00
## BoxID1056 BoxID1057 BoxID1058
## 3.297124e-01 1.878840e-02 NA
## BoxID1059 BoxID1060 BoxID1061
## 2.259599e-01 -7.992984e-01 -2.402620e+00
## BoxID1062 BoxID1063 BoxID1064
## 3.888723e-01 -3.264496e-01 -1.589096e+00
## BoxID1065 BoxID1066 BoxID1067
## 8.988786e-02 -4.156094e-01 -1.336534e+00
## BoxID1068 BoxID1069 BoxID1070
## -3.261881e-01 3.135752e-02 2.849739e-01
## BoxID1071 BoxID1072 BoxID1073
## NA -9.217362e-01 -5.917434e-01
## BoxID1074 BoxID1075 BoxID1076
## -1.285542e-01 -5.813427e-01 -5.013591e-01
## BoxID1078 BoxID1079 BoxID1080
## -4.576830e-01 -2.097190e+00 NA
## BoxID1081 BoxID1085 BoxID1086
## -2.839794e-01 6.433033e-01 2.720691e-01
## BoxID1087 BoxID1088 BoxID1089
## -3.945900e-01 -8.457582e-01 8.222468e-02
## BoxID1090 BoxID1091 BoxID1092
## NA -2.467177e-01 -5.527427e-01
## BoxID1093 BoxID1094 BoxID1095
## NA NA -8.257777e-01
## BoxID1096 BoxID1097 BoxID1098
## -7.405664e-01 -1.885996e+00 -7.402826e-02
## BoxID1101 BoxID1102 BoxID1103
## -2.399478e+00 -1.775544e+00 -2.992412e-01
## BoxID1104 BoxID1105 BoxID1107
## -4.734736e-02 -1.002926e+00 -3.658512e-01
## BoxID1108 BoxID1109 BoxID1110
## 5.870310e-01 -1.371029e+00 NA
## BoxID1112 BoxID1115 BoxID1116
## 2.114275e+00 -2.299762e-02 1.199895e+00
## BoxID1117 BoxID1118 BoxID1119
## -1.777662e-01 -7.438118e-02 1.729313e-01
## BoxID1120 SA0:IndicG0 SA1:IndicG0
## NA 2.660122e-01 NAvcov(m3)[420:423,420:423]## BoxID1119 BoxID1120 SA0:IndicG0 SA1:IndicG0
## BoxID1119 4.922688e-01 NA 5.750740e-17 NA
## BoxID1120 NA NA NA NA
## SA0:IndicG0 5.750740e-17 NA 1.356993e-02 NA
## SA1:IndicG0 NA NA NA NAtest <- vcov(m3)[418:423,418:423]
sqrt(test)## BoxID1117 BoxID1118 BoxID1119 BoxID1120 SA0:IndicG0
## BoxID1117 7.016187e-01 4.961194e-01 4.961194e-01 NA 7.476903e-09
## BoxID1118 4.961194e-01 7.016187e-01 4.961194e-01 NA 7.679951e-09
## BoxID1119 4.961194e-01 4.961194e-01 7.016187e-01 NA 7.583363e-09
## BoxID1120 NA NA NA NA NA
## SA0:IndicG0 7.476903e-09 7.679951e-09 7.583363e-09 NA 1.164900e-01
## SA1:IndicG0 NA NA NA NA NA
## SA1:IndicG0
## BoxID1117 NA
## BoxID1118 NA
## BoxID1119 NA
## BoxID1120 NA
## SA0:IndicG0 NA
## SA1:IndicG0 NA#se SA0IndicG0 = 0.11936296 avec lmer
#se SA0:IndicG0 = 1.16490 avec lm (sans interaction)
#MYSELF
m3 <- lm(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA*IndicG0 + SA + BoxID, data = data_PREF_three)
#Estimates SA
cf <-coef(summary(m3,complete = TRUE))
indic <- grep("SA",rownames(cf))
SAcoef <- cf[indic,1]
#names(SAcoef) <- c("SAGen", "SANonGen")
SAcoef## SA1 SA1:IndicG0
## 0.3842848 -0.2660122#Estimates se
indic <- grep("SA",rownames(vcov(m3)))
seSAcoef <- sqrt(diag(vcov(m3)[indic,indic]))
#names(SAcoef) <- c("seSAGen", "seSANonGen")
seSAcoef## SA1 SA1:IndicG0
## 0.07951066 0.116490035.2.3 Plot
(PLOT_pref_G0 <- plot_RTP_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", gen = "G0"))
(PLOT_pref_G2 <- plot_RTP_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", gen = "G2"))
(PLOT_GEN_pref_G0 <- plot_Genetic_Nongenetic_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", effect = "Non-genetic"))
(PLOT_GEN_pref_G2 <- plot_Genetic_Nongenetic_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", effect = "Genetic"))
6 PLOT LOCAL ADAPTATION
legend <- lemon::g_legend(PLOT_pref_G0)
#
#
# ## ALL GENERATIONS
# LOCAL_ADAPTATION_PLOT <- cowplot::ggdraw() +
# cowplot::draw_plot(PLOT_pref_G0 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.01, y = 0.5, width = 0.25, height = 0.45) +
# cowplot::draw_plot(PLOT_eggs_G0 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.31, y = 0.5, width = 0.25, height = 0.45) +
# cowplot::draw_plot(PLOT_rate_G0 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.61, y = 0.5, width = 0.25, height = 0.45) +
# cowplot::draw_plot(legend, x = 0.93, y = 0.5, width = 0.0001, height = 0.0001) +
# cowplot::draw_plot(PLOT_pref_G2 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.01, y = 0, width = 0.25, height = 0.45) +
# cowplot::draw_plot(PLOT_eggs_G2 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.31, y = 0, width = 0.25, height = 0.45) +
# cowplot::draw_plot(PLOT_rate_G2 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.61, y = 0, width = 0.25, height = 0.45) +
# cowplot::draw_plot_label(c("Generation: G0","Generation: G0","Generation: G1", "A", "B", "C", " ",
# "Generation: G2","Generation: G2","Generation: G3", "D", "E", "F", " "),
# x = c(0.1,0.4,0.7, 0.01, 0.30, 0.61, 0.92, 0.10,0.4,0.7, 0.01, 0.3, 0.61, 0.92),
# y = c(1,1,1, 0.98, 0.98, 0.98, 0.98, 0.5, 0.5, 0.5, 0.48, 0.48, 0.48, 0.48),
# hjust = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0),
# vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
# size = 16)
# LOCAL_ADAPTATION_PLOT
## ALL GENERATIONS
LOCAL_ADAPTATION_PLOT <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_pref_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.0, y = 0.66, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_eggs_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.0, y = 0.33, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_rate_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_pref_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0.66, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_eggs_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0.33, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_rate_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0, width = 0.4, height = 0.3) +
cowplot::draw_plot(legend, x = 0.915, y = 0.5, width = 0.000001, height = 0.000001) +
cowplot::draw_plot_label(c("Generation: G0/G1","Generation: G2/G3","A", "B", "C",
"D", "E", "F"),
x = c(0.12,0.55, 0, 0.44, 0, 0.44, 0, 0.44 ),
y = c(1,1, 0.99, 0.99, 0.66, 0.66, 0.33, 0.33),
hjust = c(0,0,0,0,0,0,0,0),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 18)
LOCAL_ADAPTATION_PLOT
cowplot::save_plot(file =here::here("figures", "Supplements_FigS4_Local_Adaptation_Residuals.pdf"),
LOCAL_ADAPTATION_PLOT,
base_height = 28/cm(1), base_width = 22/cm(1), dpi = 610)7 PLOT GENETIC vs PLASTIC
legend <- lemon::g_legend(PLOT_pref_G0)
# Genetic_NonGenetic_PLOT <- cowplot::ggdraw() +
# cowplot::draw_plot(PLOT_GEN_pref_G2 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.01, y = 0.5, width = 0.25, height = 0.45) +
# cowplot::draw_plot(PLOT_GEN_eggs_G2 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.31, y = 0.5, width = 0.25, height = 0.45) +
# cowplot::draw_plot(PLOT_GEN_rate_G2 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.61, y = 0.5, width = 0.25, height = 0.45) +
# cowplot::draw_plot(legend, x = 0.93, y = 0.5, width = 0.0001, height = 0.0001) +
# cowplot::draw_plot(PLOT_GEN_pref_G0 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.01, y = 0, width = 0.25, height = 0.45) +
# cowplot::draw_plot(PLOT_GEN_eggs_G0 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.31, y = 0, width = 0.25, height = 0.45) +
# cowplot::draw_plot(PLOT_GEN_rate_G0 + theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.61, y = 0, width = 0.25, height = 0.45) +
# cowplot::draw_plot_label(c("Genetic effects", "A", "B", "C", " ",
# "Plastic effects", "D", "E", "F", " "),
# x = c(0.4, 0.01, 0.30, 0.61, 0.92, 0.4, 0.01, 0.3, 0.61, 0.92),
# y = c(1, 0.98, 0.98, 0.98, 0.98, 0.5, 0.48, 0.48, 0.48, 0.48),
# hjust = c(0,0,0,0,0,0,0,0,0,0),
# vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
# size = 16)
# Genetic_NonGenetic_PLOT
#
#
# cowplot::save_plot(file =here::here("figures", "GENETIC_NONGENETIC_First_Third.pdf"),
# Genetic_NonGenetic_PLOT,
# base_height = 18/cm(1), base_width = 34/cm(1), dpi = 610)
#
#
#
## ALL GENERATIONS
Genetic_NonGenetic_PLOT <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_GEN_pref_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.0, y = 0.66, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_GEN_eggs_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.0, y = 0.33, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_GEN_rate_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_GEN_pref_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0.66, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_GEN_eggs_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0.33, width = 0.4, height = 0.3) +
cowplot::draw_plot(PLOT_GEN_rate_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0, width = 0.4, height = 0.3) +
cowplot::draw_plot(legend, x = 0.915, y = 0.5, width = 0.000001, height = 0.000001) +
cowplot::draw_plot_label(c("Genetic effects","Plastic effects","A", "B", "C",
"D", "E", "F"),
x = c(0.12,0.55, 0, 0.44, 0, 0.44, 0, 0.44 ),
y = c(1,1, 0.99, 0.99, 0.66, 0.66, 0.33, 0.33),
hjust = c(0,0,0,0,0,0,0,0),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 18)
Genetic_NonGenetic_PLOT
#
cowplot::save_plot(file =here::here("figures", "Fig5_GENETIC_NONGENETIC_First_Third.pdf"),
Genetic_NonGenetic_PLOT,
base_height = 28/cm(1), base_width = 22/cm(1), dpi = 610)8 PLOT POP EFFECT - HETEROGENEITY
# library("dplyr")
PPMR_ALL_Pref_Stim_G0 <- plot_realdata(dataset = data_PERF,
trait="Nb_eggs" ,
printcor = TRUE,
gen = "G0" ,
xlim = c(0,94),
ylim = c(0,94),
formula_Blanquart="log(Nb_eggs+1) ~ Test_environment + Population + SA + Test_environment:Original_environment",
xaxis_labelprint = "Fecundity in original",
yaxis_labelprint = "Fecundity in alternative")## [1] "Converting test_environment column into a factor"
## [1] "Converting original_environment column into a factor"PPMR_ALL_Pref_Stim_G2 <- plot_realdata(dataset = data_PERF,
printcor = TRUE,
trait="Nb_eggs" ,
formula_Blanquart="log(Nb_eggs+1) ~ Test_environment + Population + SA + Test_environment:Original_environment",
gen = "G2" ,
xaxis_labelprint = "Fecundity in original",
yaxis_labelprint = "Fecundity in alternative")## [1] "Converting test_environment column into a factor"
## [1] "Converting original_environment column into a factor"
## [1] "Populations that do not have measures in sympatry have been removed"PPMR_ALL_Pref_G0 <- plot_realdata(dataset = data_PREF_three,
printcor = TRUE,
trait="Nb_eggs" ,
formula_Blanquart="log(Nb_eggs+1) ~ Test_environment + Population + SA +
Test_environment:Original_environment + BoxID",
gen = "G0" ,
xaxis_labelprint = "Oviposition preference in original",
yaxis_labelprint = "Oviposition preference in alternative")## [1] "Converting test_environment column into a factor"
## [1] "Converting original_environment column into a factor"PPMR_ALL_Pref_G2 <- plot_realdata(dataset = data_PREF_three,
trait="Nb_eggs" ,
formula_Blanquart="log(Nb_eggs+1) ~ Test_environment + Population + SA +
Test_environment:Original_environment + BoxID",
gen = "G2" ,
printcor = TRUE,
xlim = c(0,80),
ylim = c(0,80),
xaxis_labelprint = "Oviposition preference in original",
yaxis_labelprint = "Oviposition preference in alternative")## [1] "Converting test_environment column into a factor"
## [1] "Converting original_environment column into a factor"PPMR_ALL_Perf_G0 <- plot_realdata(dataset = data_PERF_Rate,
trait="Rate" ,
gen = "G0" ,
printcor = TRUE,
xlim = c(0,1.1),
ylim = c(0,1.1),
formula_Blanquart="asin(sqrt(Rate)) ~ Test_environment + Population + SA + log(Nb_eggs) + Test_environment:Original_environment",
xaxis_labelprint = "Offspring performance in original",
yaxis_labelprint = "Offspring performance in alternative")## [1] "Converting test_environment column into a factor"
## [1] "Converting original_environment column into a factor"PPMR_ALL_Perf_G2 <- plot_realdata(dataset = data_PERF_Rate,
trait="Rate" ,
printcor = TRUE,
gen = "G2" ,
formula_Blanquart="asin(sqrt(Rate)) ~ Test_environment + Population + SA + log(Nb_eggs) + Test_environment:Original_environment",
xlim = c(0,0.7),
ylim = c(0,0.7),
xaxis_labelprint = "Offspring performance in original",
yaxis_labelprint = "Offspring performance in alternative")## [1] "Converting test_environment column into a factor"
## [1] "Converting original_environment column into a factor"
## [1] "Populations that do not have measures in sympatry have been removed"legend <- lemon::g_legend(PPMR_ALL_Perf_G2)
## ALL GENERATIONS
PPMR_ALL <- cowplot::ggdraw() +
cowplot::draw_plot(PPMR_ALL_Pref_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.0, y = 0.66, width = 0.4, height = 0.3) +
cowplot::draw_plot(PPMR_ALL_Pref_Stim_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.0, y = 0.33, width = 0.4, height = 0.3) +
cowplot::draw_plot(PPMR_ALL_Perf_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0, width = 0.4, height = 0.3) +
cowplot::draw_plot(PPMR_ALL_Pref_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0.66, width = 0.4, height = 0.3) +
cowplot::draw_plot(PPMR_ALL_Pref_Stim_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0.33, width = 0.4, height = 0.3) +
cowplot::draw_plot(PPMR_ALL_Perf_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.44, y = 0, width = 0.4, height = 0.3) +
cowplot::draw_plot(legend, x = 0.915, y = 0.5, width = 0.000001, height = 0.000001) +
cowplot::draw_plot_label(c("Generation G0/G1","Generation G2/G3","A", "B", "C",
"D", "E", "F"),
x = c(0.12,0.55, 0, 0.44, 0, 0.44, 0, 0.44 ),
y = c(1,1, 0.99, 0.99, 0.66, 0.66, 0.33, 0.33),
hjust = c(0,0,0,0,0,0,0,0),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 18)
PPMR_ALL
#
# cowplot::save_plot(file =here::here("figures", "Fig4_POP_effect_ALL.pdf"),
# PPMR_ALL,
# base_height = 28/cm(1), base_width = 22/cm(1), dpi = 610)